S-Nitrosylation Proteome Profile of Peripheral Blood Mononuclear Cells in Human Heart Failure.

Sue-Jie Koo,John R Petersen,Nisha Jain Garg,Shivali Gupta,Maria P Zago,Allan R Brasier,Kizhake V Soman,John E Wiktorowicz,Heidi M Spratt,Susan Stafford,Muge N Kuyumcu-Martinez

doi:10.1155/2016/1384523

Abstract

Nitric oxide (NO) protects the heart against ischemic injury; however, NO- and superoxide-dependent S-nitrosylation (S-NO) of cysteines can affect function of target proteins and play a role in disease outcome. We employed 2D-GE with thiol-labeling FL-maleimide dye and MALDI-TOF MS/MS to capture the quantitative changes in abundance and S-NO proteome of HF patients (versus healthy controls, n = 30/group). We identified 93 differentially abundant (59-increased/34-decreased) and 111 S-NO-modified (63-increased/48-decreased) protein spots, respectively, in HF subjects (versus controls, fold-change | ≥1.5|, p ≤ 0.05). Ingenuity pathway analysis of proteome datasets suggested that the pathways involved in phagocytes' migration, free radical production, and cell death were activated and fatty acid metabolism was decreased in HF subjects. Multivariate adaptive regression splines modeling of datasets identified a panel of proteins that will provide >90% prediction success in classifying HF subjects. Proteomic profiling identified ATP-synthase, thrombospondin-1 (THBS1), and vinculin (VCL) as top differentially abundant and S-NO-modified proteins, and these proteins were verified by Western blotting and ELISA in different set of HF subjects. We conclude that differential abundance and S-NO modification of proteins serve as a mechanism in regulating cell viability and free radical production, and THBS1 and VCL evaluation will potentially be useful in the prediction of heart failure.

Highlights

Of the 57 million global deaths annually, 17.3 million (∼30%) are due to cardiovascular diseases [1, 2]
The protein datasets were analyzed by ingenuity pathway analysis and Multivariate Adaptive Regression Splines (MARS) modeling, and selected proteins were confirmed for differential abundance and S-Nitric oxide (NO) modification levels by multiple assays. (b) Two-dimensional gel images of protein spots in PBMCs of heart failure (HF) subjects and normal healthy controls
The prediction success showed the CV and 80/20 models fitted perfectly on the training dataset (AUC/receiver operator characteristics (ROC): 1.00) and by >75% on the testing dataset (AUC/ROC: 0.75 for CV and 0.857 for 80/20) (Figures 5(c) and 5(d)). These analyses suggested that PBMC changes in abundance and S-NO modification of the selected protein spots will have high specificity and sensitivity in predicting the risk of Heart failure (HF)